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GalSBI Phenomenological galaxy population model for cosmology using simulation-based inference

S Fischbacher, T Kacprzak, L Tortorelli, B Moser… - arXiv preprint arXiv …, 2024 - arxiv.org
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… We present GalSBI, a phenomenological model of the galaxy population for cosmological applications using simulation-based inference. The model is based on analytical …

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@article{2412.08701v4,
Author        = {Silvan Fischbacher and Tomasz Kacprzak and Luca Tortorelli and Beatrice Moser and Alexandre Refregier and Patrick Gebhardt and Daniel Gruen},
Title         = {GalSBI: Phenomenological galaxy population model for cosmology using
  simulation-based inference},
Eprint        = {2412.08701v4},
DOI           = {10.1088/1475-7516/2025/06/007},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.CO},
Abstract      = {We present GalSBI, a phenomenological model of the galaxy population for
cosmological applications using simulation-based inference. The model is based
on analytical parametrizations of galaxy luminosity functions, morphologies and
spectral energy distributions. Model constraints are derived through iterative
Approximate Bayesian Computation, by comparing Hyper Suprime-Cam deep field
images with simulations which include a forward model of instrumental,
observational and source extraction effects. We developed an emulator trained
on image simulations using a normalizing flow. We use it to accelerate the
inference by predicting detection probabilities, including blending effects and
photometric properties of each object, while accounting for background and PSF
variations. This enables robustness tests for all elements of the forward model
and the inference. The model demonstrates excellent performance when comparing
photometric properties from simulations with observed imaging data for key
parameters such as magnitudes, colors and sizes. The redshift distribution of
simulated galaxies agrees well with high-precision photometric redshifts in the
COSMOS field within $1.5\sigma$ for all magnitude cuts. Additionally, we
demonstrate how GalSBI's redshifts can be utilized for splitting galaxy
catalogs into tomographic bins, highlighting its potential for current and
upcoming surveys. GalSBI is fully open-source, with the accompanying Python
package, $\texttt{galsbi}$, offering an easy interface to quickly generate
realistic, survey-independent galaxy catalogs.},
Year          = {2024},
Month         = {Dec},
Note          = {JCAP06(2025)007},
Url           = {http://arxiv.org/abs/2412.08701v4},
File          = {2412.08701v4.pdf}
}

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